ArticleCancers2021
Automatic Segmentation of Metastatic Breast Cancer Lesions on
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Evaluating Automated Tools for Lesion Detection onRadiology. Imaging cancer · 2025Trial
- Automatic metabolic breast cancer staging using [¹⁸F]FDG PET/CT: comparison with nuclear medicine physician-based and clinical staging.European journal of nuclear medicine and molecular imaging · 2026Article
- Automatic computation of breast cancer biomarkers from multiple [Formula: see text] F-FDG PET image segmentation.Scientific reports · 2026Article
- Advances in Functional and Metabolic Imaging for Early Tumor Treatment Response and Resistance Evaluation: A Review.Cancers · 2026Review
- Prediction of bone oligometastases in breast cancer using models based on deep learning radiomics of PET/CT imaging.Frontiers in oncology · 2025Article
- Deep learning for oncologic treatment outcomes and endpoints evaluation from CT scans in liver cancer.NPJ precision oncology · 2024Article
- The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.Journal of personalized medicine · 2024Review
- Current Role and Future Prospects of Positron Emission Tomography (PET)/Computed Tomography (CT) in the Management of Breast Cancer.Medicina (Kaunas, Lithuania) · 2024Review
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Review
- The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [Journal of imaging · 2023Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SULpeak, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SULpeak, with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients' response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.